No abstract

An exergetic analysis was performed for a 116-MW gas-turbine power plant. Mass and energy conservation laws were applied to each component of the system. Quantitative exergy balance for each component and for the whole system was considered. In this study, the exergy of a material stream is decomposed into thermal, mechanical and chemical exergy and an entropy-production flow. The effect of a change in the inlet turbine temperature on the exergetic efficiency and exergy destruction in the plant was evaluated. The crucial dependency of the exergetic efficiency and the exergy destruction on the change in the turbine inlet temperature was confirmed.

Purpose. In the present article, a new approach of the energy grid studies is introduced to program energy carriers. In this view, aproper plan is designed on the use of energy carriers considering the energy optimum use. Indeed, the proper energy grid is designed by applying Iran energy balance sheet information. It is proper to mention that, the energy grid modelling is done in a matrix form. The electrical energy distribution among power stations is achieved by using the particle swarm optimization algorithm. In the present paper, concerning the dynamic programming method, it is tried to determine a suitable combination of energy carriers. References 16, tables 17, figures 1. Key words: particle swarm optimization, final energy consumption, energy planning, energy carriers, dynamic programing. Цель. В настоящей статье предлагается новый подход к исследованию энергетических сетей для планирования энергоносителей. С этой целью разработан корректный план использования энергоносителей с учетом оптимального потребления энергии. Разработана соответствующая энергосистема с использованием информации о энергетическом баланса Ирана. Необходимо отметить, что моделирование энергосистемы выполняется в матричной форме. Распределение электрической энергии между электростанциями достигается за счет использования алгоритма оптимизации методом роя частиц. В настоящей работе, посвященной методу динамического программирования, предпринята попытка определить подходящую комбинацию энергоносителей.Библ. 16, табл. 17, рис. 1. Ключевые слова: оптимизация методом роя частиц, конечное потребление энергии, планирование в энергетике, энергоносители, динамическое программирование.

The novel 2019 coronavirus disease (COVID-19) has infected over 141 million people worldwide since April 20, 2021. More than 200 countries around the world have been affected by the coronavirus pandemic. Screening for COVID-19, we use fast and inexpensive images from computed tomography (CT) scans. In this paper, ResNet-50, VGG-16, convolutional neural network (CNN), convolutional auto-encoder neural network (CAENN), and machine learning (ML) methods are proposed for classifying Chest CT Images of COVID-19. The dataset consists of 1252 CT scans that are positive and 1230 CT scans that are negative for COVID-19 virus. The proposed models have priority over the other models that there is no need of pre-trained networks and data augmentation for them. The classification accuracies of ResNet-50, VGG-16, CNN, and CAENN were obtained 92.24%, 94.07%, 93.84%, and 93.04% respectively. Among ML classifiers, the nearest neighbor (NN) had the highest performance with an accuracy of 94%.

Purpose. In this paper, for simultaneous placement of distributed generation (DG) and capacitors, a new approach based on Spring Search Algorithm (SSA), is presented. This method is contained two stages using two sensitive index Sv and Ss. Sv and Ss are calculated according to nominal voltage and network losses. In the first stage, candidate buses are determined for installation DG and capacitors according to Sv and Ss, Then in the second stage, placement and sizing of distributed generation and capacitors are specified using SSA. The spring search algorithm is among the optimization algorithms developed by the idea of laws of nature and the search factors are a set of objects. The proposed algorithm is tested on 33-bus and 69-bus radial distribution networks. The test results indicate good performance of the proposed method. References 30, tables 4, figures 4.